Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model

Tiago Filipe Teixeira dos Santos, Stefan Schrunner, Bernhard Geiger, Olivia Pfeiler, Anja Zernig, Andre Kaestner, Roman Kern

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results.

Original languageEnglish
Article number8689102
Pages (from-to)190-198
Number of pages9
JournalIEEE transactions on semiconductor manufacturing
Volume32
Issue number2
DOIs
Publication statusPublished - May 2019

Fingerprint

pattern recognition
image processing
Feature extraction
Image processing
analogs
Specifications
specifications
Image reconstruction
wafers
Automation
Availability
Semiconductor materials
automation
restoration
availability
manufacturing
industries
engineering
deviation
Industry

Keywords

  • Automation
  • data processing
  • feature extraction
  • unsupervised learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

Cite this

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model. / Teixeira dos Santos, Tiago Filipe; Schrunner, Stefan; Geiger, Bernhard; Pfeiler, Olivia; Zernig, Anja; Kaestner, Andre; Kern, Roman.

In: IEEE transactions on semiconductor manufacturing, Vol. 32, No. 2, 8689102, 05.2019, p. 190-198.

Research output: Contribution to journalArticleResearchpeer-review

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